Introduction

The non-response bias is a bias that occurs due to systematic differences between respondents and non-respondents. To better understand the non-response bias, insights on response rates and refusal to participate in research are needed, e.g., to assess whether participants are representative of the target population and to find out whether estimates are reliable. To date, little is known on people who refuse to participate, precisely because they decline participation. This is especially true for some hard-to-reach and vulnerable populations, such as detained persons.

Previous studies used late respondents as a proxy for non-respondents or compared respondents to non-respondents when information is available from the census population. For example, in their well-designed study in the substance use field, Studer et al. [1] concluded that non-respondents, late respondents, and respondents were significantly different from each other on substance use variables, with a non-negligible non-response bias. Other studies focusing on health-related topics, such as psychiatry [2, 3], suicide [4], chronic diseases [5], and sexually transmitted diseases [6, 7] obtained similar conclusions.

Overall, non-respondents appeared as more vulnerable than respondents. In the aforementioned studies, they displayed higher rates of substance use, psychiatric morbidity [1–4, 8], severe chronic and infectious diseases [5–7], and lower health literacy prevalence estimates [9]. In addition, non-respondents usually display specific socioeconomic characteristics. For instance, they are more likely to come from deprived backgrounds and low socioeconomic strata [7, 10–12].

Unfortunately, we know little about the non-response bias in detained populations, a vulnerable hard-to-reach population, with a severe burden of diseases and barriers to health care [13–16]. This study therefore aimed to investigate the potential non-response bias among detained persons, comparing participants who accepted to sign a one-time general informed consent to those who refused. The one-time informed consent was used as a proxy to understand the non-response bias, as information on detained persons who decline study participation is usually not available.

Materials and methods

Statistical analyses

We first computed descriptive statistics for all variables (percentages and frequencies for binary variables, means and standard deviations for continuous variables). We also computed the relative bias as indicated in Studer et al. [1]: Where is the non-response rate, the mean (or proportion) for refusers, the mean (or proportion) for consenters, and the mean for the whole sample. We reported absolute values, as the sign did not affect the magnitude of the bias and was not of interest for our study. Second, we assessed the bivariate associations between acceptance to sign the informed consent and participants’ characteristics (i.e., sociodemographic variables, health literacy, and clinical information) using simple logistic regressions. Third, we performed a multivariable logistic regression including all covariates using least absolute shrinkage and selection operator (lasso) to select the most important predictors of acceptance to sign the informed consent. We also controlled for the study intervention (randomization in the booklet or video groups). Lasso is a reliable way to select variables, enhancing prediction accuracy and interpretability of the model [20], without the limitations of stepwise regression [21]. In this study, lasso selection was used in the multivariable model because the sample size was rather small in comparison with the number of covariables. No replacement was made for missing values (see Table 1 for detailed information on missing values). Statistical significance was set at p < .05. The analyses were performed with Stata 17.

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Table 1. Descriptive characteristics of consenters and refusers.

https://doi.org/10.1371/journal.pone.0282083.t001

Results

Descriptive statistics are reported in Table 1. Participants were on average 35.0 ± 11.8 years. Most of them came from countries outside the EU (63.6%), had a secondary or tertiary level of education (87.2%), and had no legal status in Switzerland (56.2%). Half of them did not have a health insurance (50.0%). They reported a high health literacy (58.5%) and few health conditions (somatic: 39.7%, psychiatric: 20.1%). Most participants chose French (57.9%) and 5.3% reported the need for another language than the ten available for the present study. No detained persons declined participation because of language difficulties. Regarding the outcome variable, a total of 83.2% signed the informed consent, whereas 16.8% declined.

We found the highest relative biases for the need of another language (39.4% for the group who answered “yes”), level of education (20.7% for the group with a primary level of education), health literacy (10.0% for the group with a low level of health literacy), region of origin (|9.2|% for the group from Switzerland or UE), health insurance (|7.8|% for the group having a health insurance), and study language (6.8% for the group that chose other languages than French).

Associations between acceptance to sign the consent and covariates are presented in Table 2. In bivariate models, consenters were more likely to come from Switzerland and EU (odd ratio [OR] = 2.75, p = .036), to have a secondary or tertiary level of education (OR = 3.23, p = .017,), to have a health insurance (OR = 2.72, p = .020), and to have a high health literacy (OR = 2.90, p = .010). They were less likely to need another study language (OR = 0.18, p = .009). There were no differences between consenters and refusers for age (p = .778), legal status in Switzerland (p = .211), and health problems (somatic: p = .496, psychiatric: p = .909). In the multivariate model after lasso selection, statistically significant predictors were level of education (OR = 2.13), health insurance status (OR = 2.04), need of another study language (OR = 0.21), and health literacy (OR = 2.20).

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Table 2. Associations between acceptance to sign the consent and covariates.

https://doi.org/10.1371/journal.pone.0282083.t002

Discussion

This study investigated the potential non-response bias among detained persons. For this purpose, we tested whether detained persons who refused to sign a one-time general informed consent were different from those who accepted.

Study findings showed that participants who declined participation displayed specific features compared to those who accepted. They were more likely to have social vulnerabilities, including educational barriers (low level of education), language and cultural barriers (especially not speaking one of the ten most common languages in the prison, but also being a migrant from outside the EU, and not speaking the region’s language), and health care-related barriers (low level of health literacy and no health insurance). These findings are in line with previous studies reporting that non-respondents were more likely to come from low socioeconomic areas, including low educational levels and migration backgrounds [7, 10–12] and that non-response bias is related to health literacy [9].

Detained persons are a population with severe social vulnerabilities, likely to come from deprived backgrounds [13]. Taken together, our findings suggested that detained persons who refused to participate in research were a more vulnerable subgroup of this already disadvantaged population.

To date, few studies investigated the association between non-response and health insurance coverage. In the USA, a study showed that non-respondents were less likely to have a health insurance coverage compared to respondents [22]. Other study findings suggested that health care use was lower in non-respondents, among people having a health insurance coverage [23]. Switzerland has a compulsory universal health care coverage, paid by the individuals. State subsidies are available to ensure that everyone can afford basic health insurance. However, in our study, half of the participants did not have a health insurance.

Another important study finding was that refusers did not display clinical vulnerability, with similar proportions of somatic and psychiatric health problems. This result was inconsistent with previous research findings in the general population, which concluded that non-respondents had more psychiatric morbidity and somatic illnesses [1, 2, 4–7]. Potential explanations of this study findings are related to the type of population and access to health care. First, the prison population might be younger than population-based studies. Therefore, detained persons are less likely to have some severe somatic chronic diseases that occur in middle age. Second, detained persons often lack access to (primary) health care before and during detention and may therefore have underdiagnosed and undertreated somatic and psychiatric diseases [24]. They are thus unaware of potential somatic or psychiatric issues. Indeed, in our sample, only 20.1% of participants self-reported mental health problems, which is probably an underestimation of the true prevalence rate of mental health problems in the prison. In a previous meta-analysis, 39.8% to 49.2% of detained persons were identified as suffering from mental illnesses [14–16].

Refusal to participate in health research is a well-known threat for study validity, but not only. It is also likely to reinforce existing health inequalities, as refusers display different characteristics and may have different study outcomes. Since refusal to participate is more frequent in vulnerable people and hard-to-reach populations, with high health needs [25] and lack of access to primary health care [26], they may be excluded from research benefits [27]. For example, the exclusion of vulnerable subgroups from clinical trials (e.g., elderly) limit the generalizability of findings, with insufficient data about positive or negative effects of treatments [28]. Such exclusion may hinder access to new treatment and high-quality care [27, 28]. Including vulnerable populations in research is critical for evidence-based medicine: It would enhance external validity, provide a fair access to research benefits, and improve understanding of vulnerabilities [25, 29].

Previous studies concluded that the non-response bias is less critical when basic sociodemographic variables of responders and non-responders are similar or when the proportion of responders exceeds 70% [30]. Our study findings suggested that with similar age and a response rate of more than 85%, non-response bias might still exist. The non-response bias should be reduced, with awareness that simple rules may not apply and efforts to encourage study participation. To improve study participation, strategies such as interview incentives, community contacts, use of different recruitment channels, and promote trusting relationships could be used [12, 25]. Guidelines are also available to improve informed consent, such as the “teach-to-goal” consent [29]. It helps to achieve a voluntary and truly informed consent.

This study had some limitations. First, the study took place at the prison medical unit. Detained persons who did not seek for health care were therefore not included (~25% of detained persons). This subgroup of detained persons was probably heterogenous, including people who did not need health care, but also people with specific vulnerabilities (e.g., language barriers, anosognosia). Second, some eligible participants declined study participation and were therefore not included in the “refusal” group (15.3%). These non-respondents could have different characteristics and our results should therefore be interpreted cautiously. The one-time informed consent was used as a proxy of non-response bias, but further studies should include detained persons who completely refuse to participate. Third, the study had a small sample size in some subgroups (i.e., participants without a legal status in Switzerland), with a potential lack of power. Fourth, data were collected among detained men. To avoid an increased gender bias in research [31], future studies should include women, even if detained persons are mostly men. Fifth, we relied on self-reports, which limited the reliability of clinical information. Use of census, administrative, and medical encounter data would be helpful to describe consenters and refusers more accurately. In addition, the reliability of self-reported information for refusers was questionable. Sixth, the S-TOFHLA assesses ability of individuals to read and understand health-related information, but it does not assess numeracy, which is an important dimension for health literacy [32]. Further studies should include a more complete range of measures of health literacy. Seventh, given important differences in sociopolitical contexts, penal systems, and prison populations, these results cannot be generalized outside Switzerland.

To conclude, the non-response bias probably occurs in prison populations, as it is the case in the general population. Detained persons who declined research participation were more likely to have social vulnerabilities. Therefore, efforts should be made to reach this vulnerable population, minimize non-response, and ensure a fair and equitable distribution of research benefits [12].

Supporting information